{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted model uses more compute or memory than your workload can justify.",
    "change": "Check for a usable pruned checkpoint and runtime support before attempting model surgery.",
    "baseline": "The original unpruned model at the same precision, context, and load.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Task quality, actual GPU memory, serving throughput, and preparation cost.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.03117",
  "title": "Exploring the Trade-Off Between Structured Pruning and Fault Tolerance in Deep Neural Networks for Space Applications",
  "summary": "The method applies iterative structured (width) pruning to DNNs and evaluates reliability via targeted bit-flip fault injection, replacing unpruned wide models in radiation-prone environments such as space hardware. It costs an additional fault-injection evaluation pipeline and increases per-inference sensitivity to single-event upsets, which the paper argues is offset by shorter execution time reducing fault exposure. The finding can fail to transfer: the reliability trade-off is specific to SEU-prone hardware and does not apply to conventional GPU or API deployments, and per-inference fault sensitivity still rises as redundancy is removed.",
  "rationale": "The paper's contribution concerns reliability under radiation-induced bit flips in space applications, which is outside the reader's production context, although structured pruning itself remains a standard, usable compression technique.",
  "technique": "Structured pruning under bit-flip fault injection",
  "family": "esparsidade_e_poda",
  "recommendation": "observar",
  "published": "2026-10-02",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.03117",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method applies iterative structured (width) pruning to DNNs and evaluates reliability via targeted bit-flip fault injection, replacing unpruned wide models in radiation-prone environments such as space hardware. It costs an additional fault-injection evaluation pipeline and increases per-inference sensitivity to single-event upsets, which the paper argues is offset by shorter execution time reducing fault exposure. The finding can fail to transfer: the reliability trade-off is specific to SEU-prone hardware and does not apply to conventional GPU or API deployments, and per-inference fault sensitivity still rises as redundancy is removed.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.03117",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "next_check": "",
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    },
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "finding": "",
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      "next_check": "",
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      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}